"Is it bad to get the answer a different way? Will they mark that as not knowing Bayes Theorem or just correct as it is an easier way to get the answer?
The way I went is to look at what happens when the factory makes 100 light bulbs. Machine A makes 60 of which 3 are faulty, Machine B makes 40 of which 1.2 are faulty. Therefore the pool of faulty lightbulbs is 3/4.2 = 5/7 from machine A and 1.2/4.2 = 3/7 from Machine B."
Will I. - "Is it bad to get the answer a different way? Will they mark that as not knowing Bayes Theorem or just correct as it is an easier way to get the answer?
The way I went is to look at what happens when the factory makes 100 light bulbs. Machine A makes 60 of which 3 are faulty, Machine B makes 40 of which 1.2 are faulty. Therefore the pool of faulty lightbulbs is 3/4.2 = 5/7 from machine A and 1.2/4.2 = 3/7 from Machine B."See full answer
"I can try to summarize their discussion as I remembered.
Linear regression is one of the method to predict target (Y) using features (X).
Formula for linear regression is a linear function of features. The aim is to choose coefficients (Teta) of the prediction function in such a way that the difference between target and prediction is least in average.
This difference between target and prediction is called loss function. The form of this loss function could be dependent from the particular real"
Ilnur I. - "I can try to summarize their discussion as I remembered.
Linear regression is one of the method to predict target (Y) using features (X).
Formula for linear regression is a linear function of features. The aim is to choose coefficients (Teta) of the prediction function in such a way that the difference between target and prediction is least in average.
This difference between target and prediction is called loss function. The form of this loss function could be dependent from the particular real"See full answer
"Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias.
Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit.
There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"
Jyoti V. - "Over-fitting of a model occurs when model fails to generalize to any new data and has high variance withing training data whereas in under fitting model isn't able to uncover the underlying pattern in the training data and high bias.
Tree based model like decision tree and random forest are likely to overfit whereas linear models like linear regression and logistic regression tends to under fit.
There are many reasons why a Random forest can overfits easily 1. Model has grown to its full depth a"See full answer
"Deep Learning is a part of Artificial Intelligence, it's like teaching the machine to think and make decisions on its own. It's like how we teach a child the concept of an apple - it's round, red, has a stem on top. We show them multiple pictures of apples and then they understand and can recognize an apple in future. Similarly, we feed lots of data to the machine, and slowly, it starts learning from that data, and can then make relevant predictions or decisions based on what it has learnt.
A co"
Surbhi G. - "Deep Learning is a part of Artificial Intelligence, it's like teaching the machine to think and make decisions on its own. It's like how we teach a child the concept of an apple - it's round, red, has a stem on top. We show them multiple pictures of apples and then they understand and can recognize an apple in future. Similarly, we feed lots of data to the machine, and slowly, it starts learning from that data, and can then make relevant predictions or decisions based on what it has learnt.
A co"See full answer
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"Number of employees after the first year = n*(1+r) = n1
Number of employees after the second year = n1(1+r) = n(1+r)**2
Hence, the number of employees after 't' years = n(1+r)*t"
Asish B. - "Number of employees after the first year = n*(1+r) = n1
Number of employees after the second year = n1(1+r) = n(1+r)**2
Hence, the number of employees after 't' years = n(1+r)*t"See full answer